Bias correction of global and regional simulated daily precipitation and surface mean temperature over Southeast Asia using quantile mapping method

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Abstract

A trend preserving quantile mapping (QM) method was applied to adjust the biases of the global and regional climate models (GCM and RCMs) simulated daily precipitation and surface mean temperature over Southeast Asia regions based on APHRODITE dataset. Output from four different RCMs as well as their driving GCM in CORDEX-EA archive were corrected to examine the added value of RCMs dynamical downscaling in the context of bias adjustment. The result shows that the RCM biases are comparable to that of the GCM biases. In some instances, RCMs amplified the GCM biases. Generally, QM method substantially improves the biases for both precipitation and temperature. However, the bias adjustment method works better for surface mean temperature and less so for daily precipitation. The large inter-models variability is reduced remarkably after bias adjustment. Overall, study indicates no strong evident that RCMs downscaling as an immediate step before bias correction provides additional improvement to the sub-regional climate compared to the correction directly carried out on their forcing GCM.

Original languageEnglish
Pages (from-to)79-90
Number of pages12
JournalGlobal and Planetary Change
Volume149
DOIs
Publication statusPublished - 1 Feb 2017

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mapping method
general circulation model
downscaling
regional climate
temperature
global climate
climate modeling
Southeast Asia

Keywords

  • Bias adjustment
  • Global climate model
  • Quantile mapping
  • Regional climate model

ASJC Scopus subject areas

  • Oceanography
  • Global and Planetary Change

Cite this

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title = "Bias correction of global and regional simulated daily precipitation and surface mean temperature over Southeast Asia using quantile mapping method",
abstract = "A trend preserving quantile mapping (QM) method was applied to adjust the biases of the global and regional climate models (GCM and RCMs) simulated daily precipitation and surface mean temperature over Southeast Asia regions based on APHRODITE dataset. Output from four different RCMs as well as their driving GCM in CORDEX-EA archive were corrected to examine the added value of RCMs dynamical downscaling in the context of bias adjustment. The result shows that the RCM biases are comparable to that of the GCM biases. In some instances, RCMs amplified the GCM biases. Generally, QM method substantially improves the biases for both precipitation and temperature. However, the bias adjustment method works better for surface mean temperature and less so for daily precipitation. The large inter-models variability is reduced remarkably after bias adjustment. Overall, study indicates no strong evident that RCMs downscaling as an immediate step before bias correction provides additional improvement to the sub-regional climate compared to the correction directly carried out on their forcing GCM.",
keywords = "Bias adjustment, Global climate model, Quantile mapping, Regional climate model",
author = "Ngai, {Sheau Tieh} and {Tangang @ Tajudin Mahmud}, Fredolin and Liew, {Ju Neng}",
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TY - JOUR

T1 - Bias correction of global and regional simulated daily precipitation and surface mean temperature over Southeast Asia using quantile mapping method

AU - Ngai, Sheau Tieh

AU - Tangang @ Tajudin Mahmud, Fredolin

AU - Liew, Ju Neng

PY - 2017/2/1

Y1 - 2017/2/1

N2 - A trend preserving quantile mapping (QM) method was applied to adjust the biases of the global and regional climate models (GCM and RCMs) simulated daily precipitation and surface mean temperature over Southeast Asia regions based on APHRODITE dataset. Output from four different RCMs as well as their driving GCM in CORDEX-EA archive were corrected to examine the added value of RCMs dynamical downscaling in the context of bias adjustment. The result shows that the RCM biases are comparable to that of the GCM biases. In some instances, RCMs amplified the GCM biases. Generally, QM method substantially improves the biases for both precipitation and temperature. However, the bias adjustment method works better for surface mean temperature and less so for daily precipitation. The large inter-models variability is reduced remarkably after bias adjustment. Overall, study indicates no strong evident that RCMs downscaling as an immediate step before bias correction provides additional improvement to the sub-regional climate compared to the correction directly carried out on their forcing GCM.

AB - A trend preserving quantile mapping (QM) method was applied to adjust the biases of the global and regional climate models (GCM and RCMs) simulated daily precipitation and surface mean temperature over Southeast Asia regions based on APHRODITE dataset. Output from four different RCMs as well as their driving GCM in CORDEX-EA archive were corrected to examine the added value of RCMs dynamical downscaling in the context of bias adjustment. The result shows that the RCM biases are comparable to that of the GCM biases. In some instances, RCMs amplified the GCM biases. Generally, QM method substantially improves the biases for both precipitation and temperature. However, the bias adjustment method works better for surface mean temperature and less so for daily precipitation. The large inter-models variability is reduced remarkably after bias adjustment. Overall, study indicates no strong evident that RCMs downscaling as an immediate step before bias correction provides additional improvement to the sub-regional climate compared to the correction directly carried out on their forcing GCM.

KW - Bias adjustment

KW - Global climate model

KW - Quantile mapping

KW - Regional climate model

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